An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting

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2011
Ceylan, Uğur
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The task of the recommendation systems is to recommend items that are relevant to the preferences of users. Two main approaches in recommendation systems are collaborative filtering and content-based filtering. Collaborative filtering systems have some major problems such as sparsity, scalability, new item and new user problems. In this thesis, a hybrid recommendation system that is based on content-boosted collaborative filtering approach is proposed in order to overcome sparsity and new item problems of collaborative filtering. The content-based part of the proposed approach exploits semantic similarities between items based on a priori defined ontology-based metadata in movie domain and derived feature-weights from content-based user models. Using the semantic similarities between items and collaborative-based user models, recommendations are generated. The results of the evaluation phase show that the proposed approach improves the quality of recommendations.
Recommender systems (Information filtering)
http://etd.lib.metu.edu.tr/upload/12613754/index.pdf
https://hdl.handle.net/11511/20770
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
U. Ceylan, “An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting,” M.S. - Master of Science, Middle East Technical University, 2011.
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